Preethi Nanjundan – författare

The purpose of this book is to discuss the trends and key drivers of Internet of Things (IoT) and artificial intelligence (AI) for automation in Industry 4.0. IoT and AI are transforming the industry thus accelerating efficiency and forging a more reliable automated enterprise. AI-driven IoT systems for Industry 4.0 explore current research to be carried out in the cutting-edge areas of AI for advanced analytics, integration of industrial IoT (IIoT) solutions and Edge components, automation in cyber-physical systems, world leading Industry 4.0 frameworks and adaptive supply chains, etc.

A thorough exploration of Industry 4.0 is provided, focusing on the challenges of digital transformation and automation. It covers digital connectivity, sensors, and the integration of intelligent thinking and data science. Emphasizing the significance of AI, the chapter delves into optimal decision-making in Industry 4.0. It extensively examines automation and hybrid edge computing architecture, highlighting their applications. The narrative then shifts to IIoT and edge AI, exploring their convergence and the use of edge AI for visual insights in smart factories. The book concludes by discussing the role of AI in constructing digital twins, speeding up product development lifecycles, and offering insights for decision-making in smart factories. Throughout, the emphasis remains on the transformative impact of deep learning and AI in automating and accelerating manufacturing processes within the context of Industry 4.0.

This book is intended for undergraduates, postgraduates, academicians, researchers, and industry professionals in industrial and computer engineering.

The purpose of this book is to discuss the trends and key drivers of Internet of Things (IoT) and artificial intelligence (AI) for automation in Industry 4.0. IoT and AI are transforming the industry thus accelerating efficiency and forging a more reliable automated enterprise. AI-driven IoT systems for Industry 4.0 explore current research to be carried out in the cutting-edge areas of AI for advanced analytics, integration of industrial IoT (IIoT) solutions and Edge components, automation in cyber-physical systems, world leading Industry 4.0 frameworks and adaptive supply chains, etc.

A thorough exploration of Industry 4.0 is provided, focusing on the challenges of digital transformation and automation. It covers digital connectivity, sensors, and the integration of intelligent thinking and data science. Emphasizing the significance of AI, the chapter delves into optimal decision-making in Industry 4.0. It extensively examines automation and hybrid edge computing architecture, highlighting their applications. The narrative then shifts to IIoT and edge AI, exploring their convergence and the use of edge AI for visual insights in smart factories. The book concludes by discussing the role of AI in constructing digital twins, speeding up product development lifecycles, and offering insights for decision-making in smart factories. Throughout, the emphasis remains on the transformative impact of deep learning and AI in automating and accelerating manufacturing processes within the context of Industry 4.0.

This book is intended for undergraduates, postgraduates, academicians, researchers, and industry professionals in industrial and computer engineering.

Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data.

The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it''s accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.

Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data.

The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it''s accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.

This book provides a thorough and comprehensive introduction to natural language processing (NLP), a critical field at the intersection of artificial intelligence and computational linguistics. It explores key techniques such as sentiment analysis, which enables the detection of emotional tone in text, machine translation, facilitating the conversion of text between languages, and named entity recognition (NER), which identifies and classifies entities like names, dates, and locations within text data.

The book delves into deep learning advancements, particularly the use of neural networks such as transformers and recurrent models, which have revolutionized NLP applications. Readers will gain insights into how these models drive innovations in areas such as text classification, language generation, and speech recognition.

In addition to technical concepts, the book also addresses the ethical considerations surrounding NLP, emphasizing the responsible use of AI technologies to mitigate issues like bias, misinformation, and privacy concerns. Practical case studies and real-world examples are included to illustrate how NLP is applied in various sectors, including healthcare, finance, and customer service.

This book is an invaluable resource for students, researchers, and industry professionals seeking to understand the foundational concepts, cutting-edge advancements, and broader implications of NLP, equipping them with the knowledge to innovate and apply these technologies effectively in their respective fields.